Results 191 to 200 of about 159,841 (265)

Multi-Modal Convolutional Parameterisation Network for Guided Image Inverse Problems. [PDF]

J Imaging
Czerkawski M, Upadhyay P, Davison C, Atkinson R, Michie C, Andonovic I, Macdonald M, Cardona J, Tachtatzis C.   +8 more
europepmc   +1 more source

Colossal Photovoltaic Current in Ferroelectric Oxide by Constructing Defect Band

Advanced Science, EarlyView.
Colossal photovoltaic current density of 34.36 mA/cm2 under 375 nm illumination has been achieved in the Pb‐deficient PbTiO3 film. The ultraviolet photoresponsivity surpasses that of all reported ferroelectric materials. This enhancement is attributed to the junction effect between the Pb‐deficient and non‐defective layers.
Yiran Sun, Zhenbang Dai, Xingzhong Cao, Peng Zhang, Chen Lin, Zihang Huang, Ruian Zhang, Yi Fu, Xuanyu Jiang, He Tian, Jingjing Xue, Kaifeng Wu, Junhui Wang, Tianqi Deng, Andrew M. Rappe, Gaorong Han, Zhaohui Ren   +16 more
wiley   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks. [PDF]

PNAS Nexus
Karnakov P, Litvinov S, Koumoutsakos P.
europepmc   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

An exciting Approach to Theoretical Spectroscopy

Advanced Science, EarlyView.
ABSTRACT Theoretical spectroscopy, and more generally, electronic‐structure theory, are powerful concepts for describing the complex many‐body interactions in materials. They cover methods from ground‐state properties to lattice excitations and light‐matter interaction, including time‐resolved variants.
Martí Raya‐Moreno, Alexander Buccheri, Noah Alexy Dasch, Nasrin Farahani, Ignacio Gonzalez Oliva, Andris Gulans, Seokhyun Hong, Manoar Hossain, Hannah Kleine, Martin Kuban, Sven Lubeck, Benedikt Maurer, Pasquale Pavone, Fabian Peschel, Daria Popova‐Gorelova, Lu Qiao, Elias Richter, Santiago Rigamonti, Ronaldo Rodrigues Pela, Maximilian Schebek, Kshitij Sinha, Daniel T. Speckhard, Jan Stutz, Sebastian Tillack, Dmitry Tumakov, Jānis Užulis, Mara Voiculescu, Cecilia Vona, Mao Yang, Claudia Draxl   +29 more
wiley   +1 more source

Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami

Advanced Science, EarlyView.
This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning, Lu Lu, Sophie Leanza, Ruike Renee Zhao   +3 more
wiley   +1 more source

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source